DataFrame.expanding(min_periods=1, freq=None, center=False, axis=0) [source] Provides expanding transformations. New in version 0.18.0. Parameters: min_periods : int, default None Minimum number of observations in window required to have a value (otherwise result is NA). freq : string or DateOffset object, optional (default None) (DEPRECATED) Frequency to conform the data to before computing the statistic. Specified as a frequency string or DateOffset object. center : boolean, defaul

Panel.rsub(other, axis=0) [source] Subtraction of series and other, element-wise (binary operator rsub). Equivalent to other - panel. Parameters: other : DataFrame or Panel axis : {items, major_axis, minor_axis} Axis to broadcast over Returns: Panel See also Panel.sub

Index.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True) [source] Returns object containing counts of unique values. The resulting object will be in descending order so that the first element is the most frequently-occurring element. Excludes NA values by default. Parameters: normalize : boolean, default False If True then the object returned will contain the relative frequencies of the unique values. sort : boolean, default True Sort by values ascending

Panel.set_value(*args, **kwargs) [source] Quickly set single value at (item, major, minor) location Parameters: item : item label (panel item) major : major axis label (panel item row) minor : minor axis label (panel item column) value : scalar takeable : interpret the passed labels as indexers, default False Returns: panel : Panel If label combo is contained, will be reference to calling Panel, otherwise a new object

DatetimeIndex.to_native_types(slicer=None, **kwargs) [source] slice and dice then format

MultiIndex.fillna(value=None, downcast=None) [source] Fill NA/NaN values with the specified value Parameters: value : scalar Scalar value to use to fill holes (e.g. 0). This value cannot be a list-likes. downcast : dict, default is None a dict of item->dtype of what to downcast if possible, or the string ?infer? which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible) Returns: filled : %(klass)s

DataFrame.update(other, join='left', overwrite=True, filter_func=None, raise_conflict=False) [source] Modify DataFrame in place using non-NA values from passed DataFrame. Aligns on indices Parameters: other : DataFrame, or object coercible into a DataFrame join : {?left?}, default ?left? overwrite : boolean, default True If True then overwrite values for common keys in the calling frame filter_func : callable(1d-array) -> 1d-array<boolean>, default None Can choose to replace va

Series.str.slice(start=None, stop=None, step=None) [source] Slice substrings from each element in the Series/Index Parameters: start : int or None stop : int or None step : int or None Returns: sliced : Series/Index of objects

DataFrameGroupBy.corr(method='pearson', min_periods=1) Compute pairwise correlation of columns, excluding NA/null values Parameters: method : {?pearson?, ?kendall?, ?spearman?} pearson : standard correlation coefficient kendall : Kendall Tau correlation coefficient spearman : Spearman rank correlation min_periods : int, optional Minimum number of observations required per pair of columns to have a valid result. Currently only available for pearson and spearman correlation Returns: y :

MultiIndex.str() [source] Vectorized string functions for Series and Index. NAs stay NA unless handled otherwise by a particular method. Patterned after Python?s string methods, with some inspiration from R?s stringr package. Examples >>> s.str.split('_') >>> s.str.replace('_', '')